Unverified50% confidenceFactExact time
The training data for large language models (LLMs) is mainly human language; if language itself carries hidden biases, then model outputs may also amplify these biases.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/25/2026
First Seen
Valid until: 1/23/2027
Sources
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Related Claims
VerifiedLLMs acquire language capabilities by performing unsupervised pre-training on massive text corpora (predicting the next token).76% similarUnverifiedFor programming languages, generalization means that even if a language does not appear directly in the training data, the LLM can transfer and reason based on commonalities across languages.75% similarUnverifiedExisting large language models (LLMs), when facing problems involving subjective cognition, can only make statistical inferences from the surface of language and find it difficult to explain mental states and their changes.74% similarUnverifiedTraditional n-gram models and neural language models trained on a single language assign extremely low probability scores to cross-lingual word sequences, causing the decoder to tend to forcibly map foreign-language words to phonetically similar words in the target language.74% similarUnverifiedThe emergent capabilities of large language models are regarded by some researchers as signals of general intelligence, while others believe these capabilities may still represent efficient compression and retrieval of implicit patterns in training data.73% similar
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